Recommender systems that predict the preference of users have attracted more and more
attention in decades. One of the most popular methods in this field is collaborative filtering …

Collaborative filtering inherently suffers from the data sparsity and cold start problems.
Social networks have been shown useful to help alleviate these issues. However, social …

Collaborative filtering algorithms, such as matrix factorization techniques, are recently
gaining momentum due to their promising performance on recommender systems. However …

In recent years, the recommendation systems have become increasingly popular and have
been used in a broad variety of applications. Here, we investigate the matrix completion …

Matrix approximation is a common model-based approach to collaborative filtering in
recommender systems. Many relevant algorithms that fuse social contextual information …

Recommender systems collect various kinds of data to create their recommendations.
Collaborative filtering is a common technique in this area. This technique gathers and …

Collaborative filtering is a popular strategy in recommender systems area. This approach
gathers users' ratings and then predicts what users will rate based on their similarity to other …

Collaborative filtering (CF), particularly matrix factorization (MF) based methods, have been
widely used in recommender systems. The literature has reported that matrix factorization …

Recommender systems have been widely studied in the literature as they have real world
impacts in many E-commerce platforms and social networks. Most previous systems are …

In recent years, the ever-growing contents (movies, clothes, books, etc.) accessible and
buyable via the Internet have led to the information overload issue and therefore the item …